Face Search at Scale: 80 Million Gallery
Dayong Wang, Charles Otto, Anil K. Jain
Abstract
Due to the prevalence of social media websites, one challenge facing computer vision researchers is to devise methods to process and search for persons of interest among the billions of shared photos on these websites. Facebook revealed in a 2013 white paper that its users have uploaded more than $250$ billion photos, and are uploading $350$ million new photos each day. Due to this humongous amount of data, large-scale face search for mining web images is both important and challenging. Despite significant progress in face recognition, searching a large collection of unconstrained face images has not been adequately addressed. To address this challenge, we propose a face search system which combines a fast search procedure, coupled with a state-of-the-art commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe face, we first filter the large gallery of photos to find the top- $k$ most similar faces using deep features generated from a convolutional neural network. The $k$ retrieved candidates are re-ranked by combining similarities from deep features and the COTS matcher. We evaluate the proposed face search system on a gallery containing $80$ million web-do
中文速览
每天有3.5亿张新照片被上传到社交媒体，如何在数十亿张网络人脸图片中快速、准确地找到特定人物，是计算机视觉领域尚未被充分解决的难题。研究团队提出了一套级联人脸搜索框架（cascaded face search system）：先用深度卷积神经网络（deep convolutional neural network）提取人脸特征，配合乘积量化（product quantization）技术从8000万张网络人脸中快速筛选出少量候选结果，再调用商业人脸识别软件（COTS matcher）对候选结果进行重排序，从而兼顾搜索速度与精度。在标准人脸识别基准测试LFW上，该系统达到98.23%的准确率；在更具挑战性的IJB-A数据集上也取得了有竞争力的成绩；在以波士顿马拉松爆炸案嫌疑人照片为查询样本的实验中，系统能在7秒内于8000万张图片库里将其锁定在第8位。这项工作首次将大规模人脸搜索实验推进到8000万量级，为社交媒体内容审核、执法调查等现实场景提供了兼具实用性与可扩展性的技术方案。
原文 arXiv:1507.07242;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1507.07242v2